ruvector/docs/gnn/graph-wasm-setup.md
rUv 4d5d3bb092 feat(micro-hnsw-wasm): Add Neuromorphic HNSW v2.3 with SNN Integration (#40)
* docs: Add comprehensive GNN v2 implementation plans

Add 22 detailed planning documents for 19 advanced GNN features:

Tier 1 (Immediate - 3-6 months):
- GNN-Guided HNSW Routing (+25% QPS)
- Incremental Graph Learning/ATLAS (10-100x faster updates)
- Neuro-Symbolic Query Execution (hybrid neural + logical)

Tier 2 (Medium-Term - 6-12 months):
- Hyperbolic Embeddings (Poincaré ball model)
- Degree-Aware Adaptive Precision (2-4x memory reduction)
- Continuous-Time Dynamic GNN (concept drift detection)

Tier 3 (Research - 12+ months):
- Graph Condensation (10-100x smaller graphs)
- Native Sparse Attention (8-15x GPU speedup)
- Quantum-Inspired Attention (long-range dependencies)

Novel Innovations (10 experimental features):
- Gravitational Embedding Fields, Causal Attention Networks
- Topology-Aware Gradient Routing, Embedding Crystallization
- Semantic Holography, Entangled Subspace Attention
- Predictive Prefetch Attention, Morphological Attention
- Adversarial Robustness Layer, Consensus Attention

Includes comprehensive regression prevention strategy with:
- Feature flag system for safe rollout
- Performance baseline (186 tests + 6 search_v2 tests)
- Automated rollback mechanisms

Related to #38

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude <noreply@anthropic.com>

* feat(micro-hnsw-wasm): Add neuromorphic HNSW v2.3 with SNN integration

## New Crate: micro-hnsw-wasm v2.3.0
- Published to crates.io: https://crates.io/crates/micro-hnsw-wasm
- 11.8KB WASM binary with 58 exported functions
- Neuromorphic vector search combining HNSW + Spiking Neural Networks

### Core Features
- HNSW graph-based approximate nearest neighbor search
- Multi-distance metrics: L2, Cosine, Dot product
- GNN extensions: typed nodes, edge weights, neighbor aggregation
- Multi-core sharding: 256 cores × 32 vectors = 8K total

### Spiking Neural Network (SNN)
- LIF (Leaky Integrate-and-Fire) neurons with membrane dynamics
- STDP (Spike-Timing Dependent Plasticity) learning
- Spike propagation through graph topology
- HNSW→SNN bridge for similarity-driven neural activation

### Novel Neuromorphic Features (v2.3)
- Spike-Timing Vector Encoding (rate-to-time conversion)
- Homeostatic Plasticity (self-stabilizing thresholds)
- Oscillatory Resonance (40Hz gamma synchronization)
- Winner-Take-All Circuits (competitive selection)
- Dendritic Computation (nonlinear branch integration)
- Temporal Pattern Recognition (spike history matching)
- Combined Neuromorphic Search pipeline

### Performance Optimizations
- 5.5x faster SNN tick (2,726ns → 499ns)
- 18% faster STDP learning
- Pre-computed reciprocal constants
- Division elimination in hot paths

### Documentation & Organization
- Reorganized docs into subdirectories (gnn/, implementation/, publishing/, status/)
- Added comprehensive README with badges, SEO, citations
- Added benchmark.js and test_wasm.js test suites
- Added DEEP_REVIEW.md with performance analysis
- Added Verilog RTL for ASIC synthesis

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude <noreply@anthropic.com>

---------

Co-authored-by: Claude <noreply@anthropic.com>
2025-12-01 22:30:15 -05:00

302 lines
6.5 KiB
Markdown

# RuVector Graph WASM - Setup Complete
## Created Files
### Rust Crate (`/home/user/ruvector/crates/ruvector-graph-wasm/`)
1. **Cargo.toml** - WASM crate configuration with dependencies
- wasm-bindgen for JavaScript bindings
- serde-wasm-bindgen for type conversions
- ruvector-core for hypergraph functionality
- Optimized release profile for small WASM size
2. **src/lib.rs** - Main GraphDB implementation
- `GraphDB` class with Neo4j-inspired API
- Node, edge, and hyperedge operations
- Basic Cypher query support
- Import/export functionality
- Statistics and monitoring
3. **src/types.rs** - JavaScript-friendly type conversions
- `JsNode`, `JsEdge`, `JsHyperedge` wrappers
- `QueryResult` for query responses
- Type conversion utilities
- Error handling types
4. **src/async_ops.rs** - Async operations
- `AsyncQueryExecutor` for streaming results
- `AsyncTransaction` for atomic operations
- `BatchOperations` for bulk processing
- `ResultStream` for chunked data
5. **build.sh** - Build script for multiple targets
- Web (ES modules)
- Node.js
- Bundler (Webpack, Rollup, etc.)
6. **README.md** - Comprehensive documentation
- API reference
- Usage examples
- Browser compatibility
- Build instructions
### NPM Package (`/home/user/ruvector/npm/packages/graph-wasm/`)
1. **package.json** - NPM package configuration
- Build scripts for all targets
- Package metadata
- Publishing configuration
2. **index.js** - Package entry point
- Re-exports from generated WASM
3. **index.d.ts** - TypeScript definitions
- Full type definitions for all classes
- Interface definitions
- Enum types
### Examples (`/home/user/ruvector/examples/`)
1. **graph_wasm_usage.html** - Interactive demo
- Live graph database operations
- Visual statistics display
- Sample graph creation
- Hypergraph examples
## API Overview
### Core Classes
#### GraphDB
```javascript
const db = new GraphDB('cosine');
db.createNode(labels, properties)
db.createEdge(from, to, type, properties)
db.createHyperedge(nodes, description, embedding?, confidence?)
await db.query(cypherQuery)
db.stats()
```
#### JsNode
```javascript
node.id
node.labels
node.properties
node.getProperty(key)
node.hasLabel(label)
```
#### JsEdge
```javascript
edge.id
edge.from
edge.to
edge.type
edge.properties
```
#### JsHyperedge
```javascript
hyperedge.id
hyperedge.nodes
hyperedge.description
hyperedge.embedding
hyperedge.confidence
hyperedge.order
```
### Advanced Features
#### Async Query Execution
```javascript
const executor = new AsyncQueryExecutor(100);
await executor.executeStreaming(query);
```
#### Transactions
```javascript
const tx = new AsyncTransaction();
tx.addOperation('CREATE (n:Person {name: "Alice"})');
await tx.commit();
```
#### Batch Operations
```javascript
const batch = new BatchOperations(1000);
await batch.executeBatch(statements);
```
## Building
### Prerequisites
1. Install Rust toolchain:
```bash
curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh
```
2. Install wasm-pack:
```bash
curl https://rustwasm.github.io/wasm-pack/installer/init.sh -sSf | sh
```
3. Add WASM target:
```bash
rustup target add wasm32-unknown-unknown
```
### Build Commands
#### Build for Web (default)
```bash
cd /home/user/ruvector/crates/ruvector-graph-wasm
./build.sh
```
Or using npm:
```bash
cd /home/user/ruvector/npm/packages/graph-wasm
npm run build
```
#### Build for Node.js
```bash
npm run build:node
```
#### Build for Bundlers
```bash
npm run build:bundler
```
#### Build All Targets
```bash
npm run build:all
```
### Using in Projects
#### Browser (ES Modules)
```html
<script type="module">
import init, { GraphDB } from './ruvector_graph_wasm.js';
await init();
const db = new GraphDB('cosine');
// Use the database...
</script>
```
#### Node.js
```javascript
const { GraphDB } = require('@ruvector/graph-wasm/node');
const db = new GraphDB('cosine');
```
#### Bundlers (Webpack, Vite, etc.)
```javascript
import { GraphDB } from '@ruvector/graph-wasm';
const db = new GraphDB('cosine');
```
## Features Implemented
- ✅ Node CRUD operations
- ✅ Edge CRUD operations
- ✅ Hyperedge support (n-ary relationships)
- ✅ Basic Cypher query parsing
- ✅ Import/export to Cypher
- ✅ Vector embeddings support
- ✅ Database statistics
- ✅ Async operations
- ✅ Transaction support
- ✅ Batch operations
- ✅ TypeScript definitions
- ✅ Browser compatibility
- ✅ Node.js compatibility
- ✅ Web Worker support (prepared)
## Roadmap
- [ ] Full Cypher parser implementation
- [ ] IndexedDB persistence
- [ ] Graph algorithms (PageRank, shortest path)
- [ ] Advanced query optimization
- [ ] Schema validation
- [ ] Full-text search
- [ ] Geospatial queries
- [ ] Temporal graph queries
## Integration with RuVector
This WASM binding leverages RuVector's hypergraph implementation from `ruvector-core`:
- **HypergraphIndex**: Bipartite graph storage for n-ary relationships
- **Hyperedge**: Multi-entity relationships with embeddings
- **TemporalHyperedge**: Time-aware relationships
- **CausalMemory**: Causal relationship tracking
- **Distance Metrics**: Cosine, Euclidean, DotProduct, Manhattan
## File Locations
```
/home/user/ruvector/
├── crates/
│ └── ruvector-graph-wasm/
│ ├── Cargo.toml
│ ├── README.md
│ ├── build.sh
│ └── src/
│ ├── lib.rs
│ ├── types.rs
│ └── async_ops.rs
├── npm/
│ └── packages/
│ └── graph-wasm/
│ ├── package.json
│ ├── index.js
│ └── index.d.ts
├── examples/
│ └── graph_wasm_usage.html
└── docs/
└── graph-wasm-setup.md (this file)
```
## Next Steps
1. **Install WASM toolchain**:
```bash
rustup target add wasm32-unknown-unknown
curl https://rustwasm.github.io/wasm-pack/installer/init.sh -sSf | sh
```
2. **Build the package**:
```bash
cd /home/user/ruvector/crates/ruvector-graph-wasm
./build.sh
```
3. **Test in browser**:
```bash
# Serve the examples directory
python3 -m http.server 8000
# Open http://localhost:8000/examples/graph_wasm_usage.html
```
4. **Publish to NPM** (when ready):
```bash
cd /home/user/ruvector/npm/packages/graph-wasm
npm publish --access public
```
## Support
- GitHub: https://github.com/ruvnet/ruvector
- Issues: https://github.com/ruvnet/ruvector/issues
- Docs: https://github.com/ruvnet/ruvector/wiki
---
**Created**: 2025-11-25
**Version**: 0.1.1
**License**: MIT